Interestingly, QKL models (in red curves) take more training epochs than DNN models (in black curves) to attain an accuracy of over 40%. Convergence of DNN-based models seems to be faster in both training and testing sets. This phenomenon could be related to the discussion of “barren plateaus” in previous QML studies [1]}, [2]}. In summary, both DNN and QKL models seem to be over-fitted at the training set as shown in the two upper dash-line curves. However, the test set performance of QKL shows a better than 15% accuracy when compared to the test set performance of DNN when comparing the two lower solid-line curves.
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